Assessing operating conditions of a receiver in a communication network based on forward error correction decoding properties

ABSTRACT

A system is configured to measure a forward error correction (FEC) decoding property associated with applying FEC decoding to FEC-encoded bits or symbols at a receiver device deployed in a communication network. The system is further configured to provide an assessment of operating conditions of the receiver device based on the FEC decoding property. In one example, the FEC decoding property comprises a distribution of a number of iterations of a FEC decoding operation applied to a plurality of FEC blocks processed within a period of time. In some examples, the FEC decoding property comprises any one of heat, temperature, current, voltage, active clock cycles, idle clock cycles, activity of parallel engines, activity of pipeline stages, and input or output buffer fill level of the FEC decoding. In some examples, the assessment is based on a comparison between the FEC decoding property and reference FEC data.

TECHNICAL FIELD

This document relates to the technical field of communications.

BACKGROUND

In a communication network, a transmitter may transmit a signal over acommunication channel to a receiver, where the signal is representativeof digital information in the form of bits or symbols. The receiver mayprocess the signal received over the communication channel to recoverestimates of the bits or symbols. The received signal may comprise adegraded version of the signal that was generated at the transmitter.Various components of the communication network may contribute to signaldegradation.

The amount of signal degradation may be characterized by signal-to-noiseratio (SNR), or alternatively by noise-to-signal ratio (NSR). A high NSRmay result in erroneous estimates of the bits. The probability that bitestimates recovered at the receiver differ from the original bitsencoded at the transmitter may be characterized by the Bit Error Ratioor Bit Error Rate (BER).

Forward Error Correction (FEC) techniques may be used to reduce the BER.The original bits of information from the client (referred to as clientbits) may undergo a FEC encoding operation based on a chosen FEC scheme.The resulting FEC-encoded bits include redundant information, such asparity or check bits. The bit estimates recovered at the receiver areestimates of the FEC-encoded bits that were generated at thetransmitter. These estimates may undergo a FEC decoding operation at thereceiver based on the chosen FEC scheme. The FEC decoding operationmakes use of the redundant information that was included in theFEC-encoded bits in order to detect and correct bit errors. Ultimately,estimates of the original client bits may be recovered from theFEC-decoded bit estimates.

FEC encoding is advantageous in that it acts to reduce the received BERwithout the need to resend data packets. However, this is at the cost ofan increased overhead. The amount of overhead or redundancy added by FECencoding may be characterized by the information rate R, where R isdefined as the ratio of the length of the input data sequence to thelength of the output data sequence after FEC encoding (which includesthe overhead). For example, if FEC encoding adds 25% overhead, then forevery four bits that are to be FEC-encoded, the FEC encoding will add 1bit of overhead, resulting in 5 FEC-encoded bits to be transmitted tothe optical receiver. This corresponds to an information rate R=4/5=0.8.

SUMMARY

According to a broad aspect, a system comprises circuitry configured tomeasure an indirect forward error correction (FEC) decoding propertyassociated with applying FEC decoding to FEC-encoded bits or symbols ata receiver device deployed in a communication network, the FEC-encodedbits or symbols having been encoded using FEC encoding corresponding tothe FEC decoding. The system further comprises circuitry configured toprovide an assessment of operating conditions of the receiver devicebased on the indirect FEC decoding property.

According to some examples, the indirect FEC decoding property comprisesa number of iterations of a FEC decoding operation applied to one ormore FEC blocks at the receiver device while the receiver device isdeployed in the communication network, each of the FEC blocks comprisinga plurality of the FEC-encoded bits or symbols.

According to some examples, the indirect FEC decoding property comprisesa distribution of the number of iterations of the FEC decoding operationapplied to each of a plurality of the FEC blocks processed within aperiod of time.

According to some examples, the indirect FEC decoding property comprisesany one of heat, temperature, current, voltage, active clock cycles,idle clock cycles, activity of parallel engines, activity of pipelinestages, input buffer fill level of the FEC decoding, and output bufferfill level of the FEC decoding.

According to some examples, the system comprises circuitry configured tostore reference FEC data, and the assessment is based on a comparisonbetween the indirect FEC decoding property and the reference FEC data.

According to some examples, the reference FEC data comprises one or moremasks.

According to some examples, the reference FEC data is based on areference FEC decoding property associated with application of the FECdecoding under predefined noise conditions.

According to some examples, the assessment of the operating conditionscomprises classification of the receiver device into at least one of oneor more predefined categories.

According to some examples, the receiver device comprises the circuitryconfigured to measure the indirect FEC decoding property, and the systemcomprises a controller device, wherein the controller device comprisescircuitry configured to initiate a change in one or more parameters ofthe communication network based on the assessment of the operatingconditions of the receiver device.

According to some examples, the parameters of the communication networkcomprise one or more of data rate, launch power, transmission distance,channel spacing, add-drop filter configuration, and network routing.

According to some examples, the controller device comprises thecircuitry configured to provide the assessment of the operatingconditions of the receiver device.

According to some examples, providing the assessment comprises one ormore of storing assessment results at one or more electronic devices inthe communication network, transmitting assessment results from oneelectronic device in the communication network to another electronicdevice in the communication network, and displaying assessment resultson a display screen of at least one electronic device in thecommunication network.

According to another broad aspect, an electronic device comprisescircuitry configured to store a measured FEC iteration distribution of anumber of iterations of a FEC decoding operation applied to each of aplurality of FEC blocks processed, within a period of time, at areceiver device deployed in a communication network, each FEC blockconsisting of FEC-encoded bits or symbols encoded using a FEC encodingoperation corresponding to the FEC decoding operation. The electronicdevice further comprises circuitry configured to store a series ofreference FEC iteration distributions, each reference FEC iterationdistribution comprising a distribution of a number of iterations of theFEC decoding operation applied to FEC blocks under different predefinednoise conditions. The electronic device further comprises circuitryconfigured to calculate, based on the measured FEC iterationdistribution and the series of reference FEC iteration distributions, anestimate of a noise distribution over the plurality of FEC blocks priorto application of the FEC decoding operation.

According to some examples, the electronic device comprises circuitryconfigured to calculate a trial FEC iteration distribution by combininga trial noise distribution having trial parameters with the series ofreference FEC iteration distributions, and circuitry configured tocalculate the estimate of the noise distribution by determining thetrial parameters which minimize a difference between the trial FECiteration distribution and the measured FEC iteration distribution.

According to some examples, the electronic device comprises circuitryconfigured to calculate a modified noise distribution based on theestimate of the noise distribution and an additional predefined noisedistribution, and circuitry configured to calculate a predicted FECiteration distribution based on the modified noise distribution and theseries of reference FEC iteration distributions.

According to some examples, the electronic device comprises circuitryconfigured to provide an assessment of a predicted change in operatingconditions of the receiver device based on the predicted FEC iterationdistribution.

According to some examples, the electronic device comprises circuitryconfigured to initiate a change in one or more parameters of thecommunication network based on the assessment.

According to another broad aspect, an electronic device comprisescircuitry configured to store a test noise distribution over a pluralityof FEC blocks, each FEC block consisting of FEC-encoded bits or symbolsencoded using a FEC encoding operation. The electronic device furthercomprises circuitry configured to store a series of reference FECiteration distributions, each reference FEC iteration distributioncomprising a distribution of a number of iterations of a FEC decodingoperation applied to FEC blocks under different predefined noiseconditions, the FEC decoding operation corresponding to the FEC encodingoperation. The electronic device further comprises circuitry configuredto calculate a predicted FEC iteration distribution based on the testnoise distribution and the series of reference FEC iterationdistributions.

According to some examples, the electronic device comprises circuitryconfigured to provide an assessment of predicted operating conditions ofa receiver device based on the predicted FEC iteration distribution.

According to some examples, the electronic device comprises circuitryconfigured to initiate a change in one or more parameters of acommunication network based on the assessment.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example communication network in accordance withsome examples of the technology disclosed herein;

FIG. 2 illustrates an example receiver device in accordance with someexamples of the technology disclosed herein.

FIG. 3 illustrates an example controller device in accordance with someexamples of the technology disclosed herein.

FIG. 4 illustrates an example measured forward error correction (FEC)iteration probability mass function (PMF) and an example reference FECiteration PMF;

FIG. 5 illustrates two example measured FEC iteration PMFs and anexample mask based on the reference FEC iteration PMF illustrated inFIG. 4;

FIG. 6 illustrates an example method for assessing operating conditionsof a receiver device deployed in a communication network using ameasured FEC decoding property in accordance with some examples of thetechnology disclosed herein;

FIG. 7 illustrates an example trial noise probability density function(PDF) and an estimated noise PDF over a plurality of FEC blocks inaccordance with some examples of the technology disclosed herein;

FIG. 8 illustrates a series of reference FEC iteration PMFs, each PMFassociated with application of a FEC decoding operation under adifferent predefined condition;

FIG. 9 illustrates an example measured FEC iteration PMF and a trial FECiteration PMF calculated using the trial noise PDF illustrated in FIG. 7and the series of reference FEC iteration PMFs illustrated in FIG. 8;

FIG. 10 illustrates an example method for calculating an estimate of anoise distribution over a plurality of FEC blocks based on a measuredFEC iteration distribution in accordance with some examples of thetechnology disclosed herein;

FIG. 11 illustrates an example estimated noise PDF over a plurality ofFEC blocks, an example additional predefined noise PDF, and an examplemodified noise PDF in accordance with some examples of the technologydisclosed herein;

FIG. 12 illustrates an example measured FEC iteration PMF and an examplepredicted FEC iteration PMF resulting from the additional predefinednoise PDF applied in FIG. 11, in accordance with some examples of thetechnology disclosed herein; and

FIG. 13 illustrates an example method for calculating a predicted FECiteration distribution based on a test noise distribution, in accordancewith some examples of the technology disclosed herein.

DETAILED DESCRIPTION

FIG. 1 illustrates an example communication network 100, in accordancewith some examples of the technology disclosed herein.

The communication network 100 may comprise at least one transmitterdevice 102 and at least one receiver device 104, where the transmitterdevice 102 is capable of transmitting signals over a communicationchannel, such as a communication channel 106, and where the receiverdevice 104 is capable of receiving signals over a communication channel,such as the communication channel 106. According to some examples, thetransmitter device 102 is also capable of receiving signals. Accordingto some examples, the receiver device 104 is also capable oftransmitting signals. Thus, one or both of the transmitter device 102and the receiver device 104 may be capable of acting as a transceiver.According to one example, the transceiver may comprise a modem.

The communication network 100 may further comprise at least onecontroller device 108, where the controller device 108 is capable oftransmitting signals to one or both of the transmitter device 102 andthe receiver device 104, or receiving signals from one or both of thetransmitter device 102 and the receiver device 104, or both.Communication of signals between the controller device 108 and thetransmitter device 102 may take place over a communication channel 110,while communication of signals between the controller device 108 and thereceiver device 104 may take place over a communication channel 112.

The communication network 100 may comprise additional elements notillustrated in FIG. 1. For example, the communication network 100 maycomprise one or more additional transmitter devices, one or moreadditional receiver devices, one or more additional controller devices,and one or more other devices or elements involved in the communicationof signals in the communication network 100.

According to some examples, the signals that are transmitted andreceived in the communication network 100 may comprise any combinationof electrical signals, optical signals, and wireless signals. Forexample, the transmitter device 102 may comprise a first opticaltransceiver, the receiver device 104 may comprise a second opticaltransceiver, and the communication channel 106 may comprise an opticalcommunication channel. According to one example, one or both of thefirst optical transceiver and the second optical transceiver maycomprise a coherent modem.

Each optical communication channel in the communication network 100 mayinclude one or more links, where each link may comprise one or morespans, and each span may comprise a length of optical fiber and one ormore optical amplifiers.

Where the communication network 100 involves the transmission of opticalsignals, the communication network 100 may comprise additional opticalelements not illustrated in FIG. 1, such as wavelength selectiveswitches, optical multiplexers, optical de-multiplexers, opticalfilters, and the like.

According to some examples, the controller device 108 may be operable totransmit signals to one or more devices in the communication network 100to cause changes in one or more parameters of the communication network100. For example, the parameters may comprise one or more of data rate,launch power, transmission distance, channel spacing, add-drop filterconfiguration, and network routing.

Various elements and effects in the communication network 100 may resultin the degradation of signals transmitted between different devices.Thus, a signal received at the receiver device 104 may comprise adegraded version of a signal transmitted by the transmitter device 102.For example, where the communication channel 106 is an opticalcommunication channel, the signal transmitted by the transmitter device102 may be degraded by optical amplifier noise, optical nonlinearity,polarization-dependent loss or gain (PDL or PDG), polarization modedispersion (PMD), frequency-dependent loss, and other effects. Thedegree of signal degradation may be characterized by signal-to-noiseratio (SNR), or alternatively by noise-to-signal ratio (NSR). Thesignals transmitted in the communication network 100 may berepresentative of digital information in the form of bits or symbols.The probability that bit estimates recovered at a receiver differ fromthe original bits encoded at a transmitter may be characterized by theBit Error Ratio or Bit Error Rate (BER). As the noise power increasesrelative to the signal power, the BER may also increase.

There may exist certain requirements with respect to the maximum BERthat is deemed tolerable in a communication network. For example, aservice level agreement (SLA) may specify that the BER at the receiverdevice 104 must not exceed 10⁻¹⁵. The use of Forward Error Correction(FEC) techniques may satisfy such a requirement by reducing the BER. Forexample, a FEC encoding operation may be applied to bits or symbols atthe transmitter device 102, thereby resulting in FEC-encoded bits orsymbols which include redundant information, such as parity or checkbits. Where the FEC encoding operation is applied at the transmitterdevice 102, the signal transmitted over the communication channel 106 isrepresentative of the FEC-encoded bits or symbols. According to someexamples, the FEC encoding may be applied to multi-bit symbols. From thedegraded version of the signal received at the receiver device 104, thereceiver device 104 may recover estimates of the FEC-encoded bits orsymbols, and may apply a FEC decoding operation to those estimates todetect and correct bit errors. The combination of a FEC encodingoperation and the corresponding FEC decoding operation are hereinreferred to as a “FEC scheme.” Stronger FEC schemes provide betterprotection (i.e., better error detection and correction) by adding moreredundancy. However, this is at the expense of a lower information rateR. Circuitry to implement stronger FEC schemes may also take up morespace, may be more costly, and may produce more heat than circuitry toimplement weaker (i.e., higher-rate) FEC schemes.

A FEC scheme may be implemented using hard decision FEC decoding or softdecision FEC decoding. In hard decision FEC decoding, using BPSK as anillustrative example, a firm decoding decision is made by comparing areceived signal to a threshold; anything above the threshold is decodedas “1” and anything below the threshold is decoded as “0”. In softdecision FEC decoding, additional probability bits are used to provide amore granular indication of the received signal; in addition todetermining whether the incoming signal is “1” or “0” based on thethreshold, the soft decision FEC decoding also provides an indication ofconfidence in the decision, using a log likelihood ratio (LLR), forexample. While soft decision FEC decoding is more noise tolerant thanhard decision FEC decoding, it is also more complicated to implement.

FEC-encoded bits or symbols transmitted from a transmitter to a receivermay be arranged into blocks, referred to herein as FEC blocks. Each FECblock may consist of a plurality of FEC-encoded bits or symbols arrangedinto rows and columns. According to one example, a single FEC block maycomprise 128×128 FEC-encoded bits. Each FEC block received at thereceiver may undergo iterative FEC decoding until all of the bit errorsin the FEC block have been corrected or until a maximum number ofiterations has been reached, whichever occurs first. Iterative FECdecoding may comprise the application of a FEC decoding algorithm toindividual rows and individual columns of a FEC block. A singleiteration of a FEC decoding operation may herein be referred to as a FECiteration. According to one example, a single FEC iteration may bedefined as the application of the FEC decoding algorithm to all rows ofthe FEC block, and then to all columns of the FEC block. According toother examples, a single FEC iteration may be defined as the applicationof the FEC decoding algorithm to only the rows of the FEC block, or onlythe columns of the FEC block. Other definitions of a single FECiteration are contemplated.

Numerous FEC decoding operations are possible. FEC decoding operationsmay involve, for example, low-density parity-check (LDPC) codes, such asthose described by Roberts et al. in U.S. Pat. No. 8,230,294; turbocodes, such as those described by Berrou et al. in French Pat. No.2747255; turbo product codes, such as those described by Pyndiah et al.in French Pat. No. 2753026; polar codes, such as those described byArikan in “A performance comparison of polar codes and Reed-Mullercodes,” IEEE Commun. Lett., Volume 12, Issue 6, 2008; product codes,such as those described by Lin et al. in “Error control coding (2^(nd)edition),” Pearson, 2004; chain decoding, such as that described byOveis Gharan et al. in U.S. Pat. Application Publication No.20190190651; block parity codes, such as those described by Roberts etal. in U.S. Pat. No. 10,200,149; compression codes, such as thosedescribed by Oveis Gharan et al. in U.S. Pat. Application PublicationNo. 20190081730; or other codes. According to some examples, pipelineddecoding may be used, whereby data is forwarded to successive stages ofthe processing hardware for successive operations to be performed.According to some examples, FEC decoding may occur in sequences ofwaves, including unidirectional waves, back-and-forth waves, or otherflow patterns. According to some examples, where infinite or tail-bitingFEC encoding is used, the FEC decoding may be applied to streams ofFEC-encoded bits, rather than blocks. According to some examples, largeFEC blocks could be used where a subset of the block is processed whilethe rest is held in memory. According to some examples, paralleldecoding of FEC blocks or streams could be used. It may be advantageousto do sub-calculations in parallel. According some examples, overlappingFEC blocks could be used, such as with staircase codes. According tosome examples, overlapping FEC structures could be used, such as withthe O-FEC standard described in document CD11-E0X entitled “OFECdescription”, SG15 Q11, Acacia Communications, et al., 2018. Accordingto some examples, FEC decoding could comprise successive passes over awindow that moves along the stream of bits. According to some examples,different FEC decoding operations may be performed upon differentsubsets of received bits. According to some examples, FEC decoding maybe concatenated, such that one form of FEC decoding follows another. Forexample, hard FEC decoding may follow soft FEC decoding. According tosome examples, analog or quantum methods of FEC decoding may be used.

Where FEC decoding is applied to blocks of FEC-encoded bits or symbols,all of the bit errors or symbol errors in a given FEC block may becorrected as a result of the receiver applying a certain number of FECiterations, N_(ITER), to the FEC block. However, the receiver may limitthe number of FEC iterations that are permitted to a maximum number,N_(ITER_MAX). The maximum number of FEC iterations, N_(ITER_MAX), may bedefined based on the amount of time required to complete a single FECiteration, the data rate, and the amount of buffering at the receiver.If the number of FEC iterations applied to a given FEC block reaches themaximum number, N_(ITER_MAX), without all of the bit errors having beencorrected, the FEC decoding is considered to have failed, and the resultis a frame error.

An increase in noise power relative to signal power may result in anincrease in BER. An increased BER places more strain on FEC decoding.For example, with an increase in NSR, there may be increase in thenumber of FEC iterations, N_(ITER), needed to correct all of the biterrors in a FEC block. As N_(ITER) approaches N_(ITER_MAX), theprobability of a frame error increases. It is of interest to keep theframe error rate (FER) extremely low. A given application may have amaximum FER that is deemed tolerable. For example, a SLA may specifythat the maximum tolerable FER is 10⁻¹². The proximity of the observedFER to the maximum tolerable FER may be referred to as the margin.Accurate knowledge of the margin is valuable because it provides anindication of how close the system is to failure.

To increase system performance (for example, by increasing data rate orpropagation distance or by decreasing power usage), it may be ofinterest for the system to exhibit a FER that is as close as possible tothe maximum tolerable FER without exceeding it. However, because frameerrors are generally very rare, it is difficult to directly measure theFER exhibited by the system.

A receiver device may be configured to store and/or output informationabout the results of the FEC decoding applied at the receiver device.For example, as the receiver device is performing FEC decoding, thereceiver device may periodically store indications of the number of bitcorrections performed within a specific period of time. The number ofbit corrections per time period may provide an indirect measure of theaverage BER of the FEC-encoded bits received in that time period. Inanother example, the average BER of the FEC-encoded bits may beestimated by a comparison of the FEC-encoded bits to the bit estimatesrecovered from the FEC decoding. In one example, the receiver device isoperable to store and/or output the average BER of the FEC-encoded bitsreceived every second.

In addition to average BER, there are other parameters that may be usedto estimate performance in an optical communication network. Forexample, extrinsic information transfer (EXIT) charts may be used toestimate performance of FEC decoding, particularly for LDPC FEC schemes.Generalized mutual information may also be used to estimate FECperformance, for example, as described by Schmalen et al. in“Performance predication of nonbinary forward error correction inoptical transmission experiments,” Journal of Lightwave Technology,Volume 35, Issue 4, 2017. Methods for monitoring phase nonlinearitiesare described, for example, by Roberts et al. in U.S. Pat. No.8,594,499. U.S. Pat. No. 7,457,538 to Strawczynski et al. describesmethods for monitoring digital performance of an optical communicationsystem. U.S. Pat. No. 5,896,391 to Solheim et al. describes using FEC tomap out error contours in a received eye. U.S. Pat. No. 7,561,797 toHarley et al. describes adding calibrated amounts of digital noise sothat measurable rates of bit errors occur and then are detected andcorrected by the FEC. The amount of noise that needs to be added toachieve some BER allows estimation of the margin. U.S. Pat. No.9,438,369 to Swinkels et al. describes an application that makes use ofmargin or SNR measurements to optimize capacity. U.S. Pat. ApplicationPublication No. 20090256622 to Roberts describes measuring thetemperature or heat due to FEC.

Where the noise on a signal is completely uncorrelated, such as additivewhite Gaussian noise (AWGN), FEC techniques generally work well forcorrection of bit errors. In such cases, the BER of the FEC-encoded bitsmay be used to provide an accurate prediction of the FER. With theassumption of AWGN and knowledge of the FEC parameters (FEC design andparameterization, block size, signal and noise quantization and scaling,and the like), the precise relationship between the BER and the FER maybe determined from computer simulations. Thus, under an assumption ofexclusively uncorrelated noise, the BER measurement provided by areceiver device may be used to predict the FER, and thus the margin.

In practice, the noise on a signal may comprise some amount ofcorrelation which may place more strain on the FEC decoding, therebyincreasing the number of FEC iterations, N_(ITER), needed to correct allof the bit errors in a given FEC block, and increasing the probabilityof a frame error. The presence of correlated noise may result in anactual FER that is higher than the FER that would be predicted from themeasured BER of the FEC-encoded bits. To account for this, networkparameters may be selected to provide extra margin between the predictedFER and the maximum tolerable FER, thereby ensuring the actual FER doesnot exceed the maximum tolerable FER. This padding of the margin maylimit system performance.

Computer simulations may be used to predict FEC performance for cases ofnon-AWGN noise sources, as well as correlated noise sources. In mostcases, it may be difficult to determine the detailed statisticalproperties of the noise on the FEC-encoded bits entering the FECdecoding in an operating modem. Uncertainty in the properties of thenoise as well as variation between applications may make it difficult tosimulate FEC performance for a range of applications.

As previously discussed, a receiver device may use FEC decoding data toprovide periodic measurements of the average BER of FEC-encoded bitsthat undergo FEC decoding within a specific period of time. A very largenumber of FEC-encoded bits may be processed during this specific periodof time. For example, during a single second, the receiver device mayprocess 10 million FEC blocks comprising 100 billion FEC-encoded bits.Thus, the periodic measurements of average BER provided by the receiverdevice may provide little to no information about relatively rare eventsthat affect individual FEC blocks. For example, a single FEC block outof the 10 million FEC blocks might suffer from a large number of biterrors, such that the FEC decoding almost results in a frame error forthat block. Yet, the average BER across all bits in the 10 million FECblocks would be virtually unaffected by the bit errors in a single FECblock, and would provide no indication of how close the system was tofailure. Furthermore, the average BER reported by the receiver devicedoes not provide information about how the noise is distributed. Forexample, it is not possible distinguish from the average BER how muchcorrelation there is in the noise.

In addition to providing information about average BER, a receiverdevice may also be configured to store and/or output other statisticalproperties of the FEC decoding. In one example, the receiver device maystore and/or output FEC iteration data determined from measurements ofthe number of FEC iterations applied to each FEC block during FECdecoding. This measured FEC iteration data may comprise, for example,one or more histograms, where each histogram is accumulated over aspecific period of time and where each histogram provides, for eachnumber N_(ITER), up to N_(ITER_MAX), a record of how many times thatnumber N_(ITER) of FEC iterations were required to correct all errors ina FEC block. In other words, the histogram represents a frequencydistribution of FEC iterations over all FEC blocks the undergo FECdecoding within the specific period of time. Each histogram may have acorresponding probability mass function (PMF), which may be determinedby normalizing the histogram by the total number of FEC blocks overwhich the histogram has been accumulated. According to some examples,the measured FEC iteration data that is stored and/or output by thereceiver device may comprise one or more PMFs, where each PMF representsa measured FEC iteration distribution over a plurality of FEC blocksreceived at the receiver device within a specific period of time, suchas one second.

In contrast to the average BER, which may provide little or noinformation about rare events that affect relatively few FEC blockswithin a very large number of FEC blocks processed over a specificperiod of time, the measured FEC iteration data may provide quantitativeinformation about every single FEC block processed within that period oftime. Returning to the previous example in which a single FEC block, outof the 10 million FEC blocks, has a large number of bit errors, the highnumber of FEC iterations, N_(ITER), applied to this particular FEC blockwould be apparent from the FEC iteration histogram (and from thecorresponding FEC iteration PMF).

Valuable information about the operating conditions of a receiver devicethat is currently deployed in a communication network may be gainedusing the FEC iteration distribution measured at the receiver. Forexample, the amount of correlated noise experienced at the receiverdevice may be determined by comparing the measured FEC iterationdistribution to a reference FEC iteration distribution. According to oneexample, the reference FEC iteration distribution may be generatedthrough computer simulations using noise with known properties. Forexample, a plurality of simulated FEC blocks may be generated based on achosen FEC scheme, with the bit errors in each simulated FEC block beingarranged according to a predefined noise distribution, such as AWGN. FECdecoding may be applied to the plurality of simulated FEC blocks togenerate a reference FEC iteration distribution. If the average BERacross the plurality simulated FEC blocks is chosen to match an averageBER measured at the receiver device deployed in the communicationnetwork, then the reference FEC iteration distribution may be directlycompared to the measured FEC iteration distribution. Such a comparisonmay be used to provide an assessment of the operating conditions of thereceiver device. Examples of how such assessments may be made will bedescribed further with respect to FIGS. 4 and 5. According to otherexamples, a reference FEC iteration distribution may be generatedthrough analytic calculations such as union bound estimates or throughcalibration using real hardware in known noise conditions. A transceivermay be arranged in known noise loading conditions and the FEC iterationdistributions measured under these known conditions could be used asreference distributions.

The examples presented herein primarily focus on the use of FECiteration data in the assessment of receiver operating conditions.However, a receiver device may alternatively or additionally make use ofother indirect properties of the FEC decoding for assessment ofoperating conditions. For the purposes of this document, “indirect FECdecoding properties” are distinguished from “direct FEC decodingproperties.” Direct FEC decoding properties may comprise direct or rawmeasurements of numbers of errors corrected, evolution of mutualinformation, and the numbers or occurrences of frames with residualerrors. For example, measurements of direct FEC decoding properties mayinclude measurements of corrected bits and/or uncorrected bits;measurements of corrected frames and/or uncorrected frames; measurementsof corrected blocks and/or uncorrected blocks; measurements of correctedevents and/or uncorrected events; and measurements of events containinguncorrected bits. In contrast, measurements of indirect FEC decodingproperties may include measurements of FEC iteration data; heat ortemperature measurements including heat dissipation and silicontemperature; current or voltage measurements including current drawn,supply voltage variance, supply voltage droop, and supply voltagepattern; data delay measurements such as data delay of a variablelatency decoder; measurements of a number or proportion of idle clockcycles of an iterative decoder; measurements of a number or proportionof active clock cycles of an iterative decoder; measurements of a numberof parallel engines active; measurements of a number of active pipelinestages or activity level of each stage of a pipelined decoder;measurements of fill level or status of input or output buffers of theFEC decoding; and the like.

While it may be of interest to lower the supply voltage or increase theclock frequency, large changes in such parameters may impair theaccuracy of the digital function, and may result in frame errors.Measurements of the relationship between FEC statistics and parameterssuch as supply voltage and clock frequency may be useful for optimizingthe selection of these parameters in consideration of observedimpairments.

In general, the technology described herein is configured to provide anassessment (or evaluation, or characterization, or classification, orinference) of the operating conditions (or behaviour, or environment, orperformance, or status) of a receiver device that is deployed in acommunication network based on a measurement of at least one indirectFEC decoding property that is associated with applying FEC decoding toFEC-encoded bits or symbols at the receiver device. According to someexamples, the assessment may additionally be based on reference FECdata, which may be associated with application of the FEC decoding underpredefined noise conditions.

A “deployed” receiver device may be understood to be a receiver device(or a transceiver device) that is installed in a communication networkfor the primary purpose of receiving (or transporting) client data. Thisis in contrast to a non-deployed receiver device, which might beevaluated using synthesized data under known conditions in a laboratory.

FIG. 2 illustrates an example receiver device 202 in accordance withsome examples of the technology disclosed herein.

The receiver device 202 is an example of the receiver device 104illustrated in FIG. 1. According to one example, the receiver device 202comprises a coherent modem.

The receiver device 202 comprises a processor 204, one or morecommunication interfaces 206, and an application specific integratedcircuit (ASIC) 210. According to some examples, the processor 204 may beoperable to monitor and control the ASIC 210. According to some examples(not shown), the receiver device 202 may comprise a memory storing codewhich is executable by the processor 204. According to some examples,the processor 204 and the memory are comprised in a digital signalprocessor (DSP). According to some examples, the receiver device 202 maycomprise one or more user interface elements (not shown), such as adisplay screen, a keyboard, a mouse, and the like.

The one or more communication interfaces 206 may comprise elements forreceiving signals from other devices, such as the transmitter device 102or the controller device 108, and for converting the received signalsinto digital signals. The communication interfaces 206 may also compriseelements for converting digital signals generated at the receiver device202 into transmittable signals, and for transmitting the transmittablesignals to other devices. The signals received at the receiver device202 and transmitted by the receiver device 202 may comprise anycombination of electrical signals, optical signals, and wirelesssignals. According to some examples, the communication interfaces 206may comprise at least one optical communication interface operable toconvert optical signals received at the receiver device 202 into digitalsignals which may be processed by the ASIC 210. Such an interface may bereferred to as an optical signal receiving interface. According to someexamples, an optical signal receiving interface may comprise apolarizing beam splitter to split received optical signals intoorthogonally-polarized components, an optical hybrid to process theorthogonally-polarized components with respect to an optical signalproduced by a laser, photodetectors to convert the outputs of theoptical hybrid to analog signals, and analog-to-digital converters(ADCs) to sample the analog signals and generate corresponding digitalsignals. According to some examples, the communication interfaces 206may alternatively or additionally comprise at least one opticalcommunication interface operable to convert digital signals generated atthe receiver device 202 into optical signals for transmission to otherdevices. Such an interface may be referred to as an optical signaltransmitting interface. According to some examples, an optical signaltransmitting interface may comprise a polarizing beam splitter to splita continuous wave optical carrier generated by a laser intoorthogonally-polarized components, digital-to-analog converters (DACs)to convert digital signals generated at the receiver device 202 intoanalog signals, electrical-to-optical modulators which use the analogsignals to modulate the orthogonally-polarized components of the CWoptical carrier, and a beam combiner to combine the modulated polarizedoptical signals into optical signals to be transmitted to other devices.

At least one communication interface 206 is operable to convert signalsreceived from a transmitter device, such as the transmitter device 102,into digital signals that are representative of FEC-encoded bits orsymbols. The receiver device 202 is operable to process the FEC-encodedbits or symbols by applying FEC decoding 214, where the FEC decoding 214corresponds to FEC encoding previously used to generate the FEC-encodedbits or symbols. According to some examples, the FEC-encoded bits orsymbols may be arranged in a plurality of FEC blocks, and the FECdecoding 214 may be implemented by iteratively applying a FEC decodingoperation to the plurality of FEC blocks. In the example of FIG. 2, theFEC decoding 214 is implemented by the ASIC 210. However, in otherexamples, the FEC decoding 214 may be implemented by afield-programmable gate array (FPGA), by a portion of a circuit, or as aresult of the processor 204 executing code stored in a memory of thereceiver device 202.

As a result of implementing the FEC decoding 214, measured FEC data 216may be stored at the receiver device 202. The measured FEC data 216 maybe stored in the ASIC 210, as shown in FIG. 2, or in a separate memory(not shown). In general, the measured FEC data 216 may comprise at leastone measurement of an indirect FEC decoding property associated with orresulting from applying the FEC decoding 214 to FEC-encoded bits orsymbols processed at the receiver device 202. According to someexamples, the indirect FEC decoding property may comprise a distributionof values associated with applying the FEC decoding 214 over a period oftime. The period of time may comprise a predefined duration. Accordingto some examples, the period of time may be on the order of one second.According to one example, the measured FEC data 216 may comprise ameasured frequency distribution of iterations of a FEC decodingoperation applied to each of a plurality of FEC blocks processed at thereceiver device 202 within a period of time. A single iteration of theFEC decoding operation, also referred to as a FEC iteration, may bedefined based the FEC decoding 214. For example, a single FEC iterationmay be defined as the application of the FEC decoding 214 to all rows ofa FEC block, and then to all columns of the FEC block. The measured FECdata 216 may comprise one or more histograms, one or more PMFs, or both,where each histogram and PMF represents a frequency distribution of FECiterations over a plurality of FEC blocks. According to other examples,the measured FEC data 216 may comprise other statistical resultsassociated with applying the FEC decoding 214.

The receiver device 202 may be configured to output the measured FECdata 216, for example, by causing the communication interface(s) 206 totransmit signals representative of the measured FEC data 216 to one ormore other devices. According to one example, the receiver device 202may provide the measured FEC data 216 to a controller device, such asthe controller device 108.

According to some examples, the receiver device 202 may store referenceFEC data 218. The reference FEC data 218 may be stored in the ASIC 210,as shown in FIG. 2, or in a separate memory (not shown). According tosome examples, the reference FEC data 218 may be received via thecommunication interface(s) 206 from one or more other devices.Alternatively, the reference FEC data 218 may be generated at thereceiver device 202, for example, as a result of processing performed bythe ASIC 210, or as a result of the processor 204 executing code storedin a memory of the receiver device 202. According to some examples, thereference FEC data 218 may comprise at least one reference FEC decodingproperty associated with application of the FEC decoding 214 underpredefined noise conditions. For example, as described previously, thereference FEC data 218 may comprise reference FEC iterationdistributions determined using computer simulations, analyticcalculations, or calibration under known conditions. The reference FECdata 218 may comprise one or more histograms, one or more PMFs, or both.

According to some examples, the receiver device 202 may be configured toapply additional processing to the measured FEC data 216 (and optionallyto the reference FEC data 218), for example, by implementing assessmentprocessing 220. The assessment processing 220 may be implemented usingthe ASIC 210, as shown in FIG. 2. According to other examples (notshown), the assessment processing 220 may be implemented by theprocessor 204 executing assessment code stored in a memory to thereceiver device 202. Execution of the assessment processing 220 maygenerate assessment results, which may be transmitted via thecommunication interface(s) 206 to one or more other devices, such as thecontroller device 108. Alternatively or additionally, the assessmentresults generated by the assessment processing 220 may be stored and/oroutput to a graphical user interface (not shown) at the receiver device202. Examples of the additional processing achieved with the assessmentprocessing 220 and the assessment results generated by the assessmentprocessing 220 are described further with respect to FIGS. 5-13.

FIG. 3 illustrates an example controller device 302 in accordance withsome examples of the technology disclosed herein.

The controller device 302 is an example of the controller device 108illustrated in FIG. 1.

The controller device 302 comprises a processor 304, one or morecommunication interfaces 306, and a memory 308. The memory 308 storescode 310 which is executable by the processor 304. According to someexamples, the processor 304 and the memory 308 are comprised in a DSP.According to some examples, the controller device 302 may comprise oneor more user interface elements (not shown), such as a display screen, akeyboard, a mouse, and the like.

The one or more communication interfaces 306 may comprise elements forreceiving signals from other devices, such as the transmitter device 102or the receiver device 104, and for converting the received signals intodigital signals. The communication interfaces 306 may also compriseelements for converting digital signals generated at the controllerdevice 302 into transmittable signals, and for transmitting thetransmittable signals to other devices. The signals received at thecontroller device 302 and transmitted by the controller device 302 maycomprise any combination of electrical signals, optical signals, andwireless signals.

The controller device 302 may be configured to initiate a change in oneor more parameters of a communication network by transmitting signals toone or more other devices in the network. For example, the code 310 maycomprise controller code 312 which, when executed by the processor 304,causes the processor 304 to generate signals for transmission over thecommunication interface(s) 306, where the signals are representative ofdata, instructions, or commands which, when received by one or moreother devices in the communication network, may cause those devices toadjust one or more parameters of the network. The network parameters mayinclude, for example, any combination of channel power, choice ofmodulation format, carrier recovery parameters or other modemparameters, routing of a channel or its neighbours, and the like.

According to some examples, at least one communication interface 306 isoperable to convert signals received from a receiver device, such as thereceiver device 202, into digital signals that are representative of themeasured FEC data 216. Execution of the code 310 by the processor 304may result in the processor 304 storing the measured FEC data 216 in thememory 308 of the controller device 302. According to one example, thecontroller device 302 may be configured to display the measured FEC data216 using a display screen (not shown). In one example, the controllerdevice 302 may receive signals representative of the measured FEC data216 in response to transmitting a request or a command to the receiverdevice 202 over one of the communication interface(s) 306. Thetransmission of such a request may be initiated by an administrator oran orchestrator.

According to some examples, the memory 308 may store the reference FECdata 218. According to some examples, the reference FEC data 218 may bereceived via the communication interface(s) 306 from one or more otherdevices, such as the receiver device 202. Alternatively, the referenceFEC data 218 may be generated as a result of the processor 304 executingthe code 310.

According to some examples, the controller device 302 may be configuredto apply additional processing to the measured FEC data 216 (andoptionally to the reference FEC data 218), for example, by executingassessment code 320. Execution of the assessment code 320 may generateassessment results, which may be transmitted via the communicationinterface(s) 306 to one or more other devices. Alternatively oradditionally, the assessment results generated by the assessment code320 may be stored and/or output to a graphical user interface (notshown) at the controller device 302. Examples of the additionalprocessing achieved with the assessment code 320 and the assessmentresults generated by the assessment code 320 are described further withrespect to FIGS. 5-13.

As previously described, the controller device 302 may generate signalsto initiate changes in network parameters. Such signals may be generatedin response to the execution of the assessment code 320. Alternatively,the signals may depend on assessment results received over thecommunication interface(s) 306 from one or more other devices, such asthe receiver device 202.

In other examples (not shown), the controller device 302 may beconfigured to implement controller processing (e.g., initiating changesin network parameters) and/or to implement assessment processing usingan ASIC (not shown) that is monitored and controlled by a processor,such as the processor 304.

FIG. 4 illustrates an example measured FEC iteration PMF and an examplereference FEC iteration PMF. The measured FEC iteration PMF has beencalculated from FEC iteration data measured at a receiver device that isdeployed in a communication network, while the reference FEC iterationPMF has been calculated, for example, using computer simulations underpredefined noise conditions. The ranges of PMF values and N_(ITER)values are for illustrative purposes only and should not be considerednecessarily limiting.

According to some examples, the measured FEC iteration PMF may be usedon its own to provide an assessment of operating conditions of thereceiver device in the communication network. According to one example,an assessment may be based on the probabilities associated with onlythose values of N_(ITER) that are greater than some threshold value,N_(ITER_LIMIT). For example, if any one of those probability values isgreater than a probability threshold, PMF_(TH), then an assessment ofthe operating conditions of the receiver device may indicate that thereceiver device is experiencing a high or unacceptable level of noise.This scenario is illustrated in FIG. 4, where N_(ITER_LIMIT)=10 andPMF_(TH)=10⁻⁵. As denoted by the arrow, the measured FEC iteration PMFhas a probability greater than PMF_(TH) when N_(ITER)>N_(ITER_LIMIT),which would result in an assessment indicating that the receiver isexperiencing an unacceptable level of noise. Other criteria may bedefined, such as requiring the sum of probabilities forN_(ITER)>N_(ITER_LIMIT) to be less than some threshold in order to makean assessment that the receiver device is operating, for example, in amanner that is consistent with expectations. The probability thresholdand the choice of N_(ITER_LIMIT) may be chosen based on the average BERand some other measure of the noise experienced by the receiver deviceunder some assumption about the properties of that noise, such as thenoise being uncorrelated AWGN.

According to some examples, a comparison between the measured FECiteration PMF and the reference FEC iteration PMF may be used tofacilitate an assessment of the operating conditions of the receiverdevice in the communication network. For example, a simple visualcomparison of the measured FEC iteration PMF to the reference FECiteration PMF may be used to determine whether the receiver device is oris not operating in pure uncorrelated noise. For example, if the twocurves are very similar to one another, an assessment may be made thatthe receiver device is operating in noise that is substantiallyuncorrelated. In such cases where the measured FEC iteration PMF issimilar to, or within a certain proximity of, the reference FECiteration PMF, the assessment of the operating conditions may indicatethat the BER measurement provided by the receiver device is expected toprovide an accurate prediction of the FER and the margin. In cases wherethe measured FEC iteration PMF differs substantially from the referenceFEC iteration PMF, the assessment of the operating conditions mayindicate that the BER measurement provided by the receiver device is notexpected to provide an accurate prediction of the FER and the margin.

According to some examples, assessments of receiver operating conditionsthat are based on comparisons between a measurement of an indirect FECdecoding property and reference FEC data may involve the use of one ormore masks.

FIG. 5 illustrates two example measured FEC iteration PMFs and anexample mask based on the reference FEC iteration PMF illustrated inFIG. 4. The ranges of PMF values and N_(ITER) values are forillustrative purposes only and should not be considered necessarilylimiting.

The dashed curve represents a measured FEC iteration PMF obtained duringa first operating condition at the receiver device, while solid curverepresents a measured FEC iteration PMF obtained during a secondoperating condition at the receiver device. As is apparent from a visualcomparison of the dashed curve to the solid curve, the noise experiencedat the receiver device during the second operating condition is placingmore strain on the FEC decoding than the noise experienced during thefirst operating condition. Higher numbers of FEC iterations are neededto correct the bit errors, as is reflected by the flaring of the solidcurve relative to the dashed curve at higher N_(ITER) values. Thisadditional strain on the FEC decoding may be caused by the distributionof bit errors, the number of errors, correlation in the noise, orcombinations thereof.

The hatched area represents an example mask which may be used as part ofthe assessment of the measured FEC iteration PMF curves. The mask mayspecify certain N_(ITER) values and respective probabilities that areindicative of one category of operating conditions, whereas any N_(ITER)values and respective probabilities that are not within, or notsubstantially within, the region specified by the mask may be indicativeof another different category of operating conditions. According to someexamples, the mask may be generated based on reference FEC iterationdata. For example, as illustrated in FIG. 5, the mask may encompass thereference FEC iteration PMF from FIG. 4, but with an additionalpredefined margin for each PMF value. According to one example, where itis determined that a measured FEC iteration PMF is entirely within orsubstantially within the mask, an assessment may be provided that theoperating conditions of the receiver device are “normal” or “asexpected” (e.g., within a tolerable level of noise or distribution ofnoise). For example, based on a comparison of the mask to the measuredFEC iteration PMF represented by the dashed curve, an assessment may beprovided that the receiver in first operating condition is operating ina noise environment that is well understood. Conversely, where it isdetermined that some portion of a measured FEC iteration PMF outside ofthe mask, an assessment may be provided that the receiver device isoperating in unacceptable or problematic conditions (e.g., beyond atolerable level of noise or distribution of noise). For example, basedon a comparison of the mask to the measured FEC iteration PMFrepresented by the solid curve, an assessment may be provided that thereceiver in the second operating condition is operating in anon-negligible amount of correlated noise. As described previously, suchas assessment may indicate that the BER measurement provided by thereceiver device in the second operating condition is not expected toprovide an accurate prediction of the FER and the margin.

Although not shown in FIG. 5, it is contemplated that additional masksmay be defined for use in the assessment of operating conditions. Forexample, in addition to (or alternatively to) the mask illustrated inFIG. 5, a further mask could be defined which specifies certaincombinations of N_(ITER) values and their respective probabilities thatwill trigger a notification or warning when they are observed, orindicate the presence of certain operating conditions.

In addition to thresholds and masks, other means may be used to assessreceiver operating conditions based on measured FEC iteration data. Forexample, transient events may be detected by observing bumps in the FECiteration distribution at higher N_(ITER) values. In another example,statistical properties of the FEC iteration distribution may becalculated, such as the mean, standard deviation, or higher moments. Inanother example, it may be possible to fit one or more functions to theFEC iteration distribution and interpret the fit parameters. In anotherexample, the FEC iteration distribution may be used as input to amachine learning algorithm, such as a neural network, which is trainedas a classifier or a regressor to estimate properties of the receiver orits environment based on the shape of the distribution.

According to some examples, the assessment of receiver operatingconditions may comprise classification into a subset of one or morepredefined categories or groups or statuses based on characteristics ofthe measured FEC data. For example, based on the FEC iterationdistribution measured at a particular receiver, the receiver could beclassified as belonging to a “normal operation” group or an “abnormaloperation” group. In another example, the measured FEC iterationdistribution could be used to classify the receiver as belonging to a“substantial nonlinear noise” group (which might manifest as flaring inthe FEC iteration distribution) or a “transient events” group (whichmight manifest as isolated bumps at higher N_(ITER) values in a FECiteration distribution that might otherwise appear similar to areference FEC iteration distribution under a known condition of AWGN).The group(s) and method(s) of classification may be defined by theassessment processing 220 or the assessment code 320. It will beapparent that a receiver device could be classified as belonging todifferent groups at different times, for example, as a result of changesin the measured FEC iteration distribution or changes in the manner ofclassification.

According to some examples, the shape of the measured FEC iterationdistribution may be used to predict continuous variables, such as theexpected FER.

FIG. 6 illustrates an example method 600 for assessing operatingconditions of a receiver device deployed in a communication networkusing a measured of an indirect FEC decoding property in accordance withsome examples of the technology disclosed herein. According to someexamples, the method 600 is performed in a communication network, suchas the communication network 100. Some aspects of the method may beperformed at a receiver device, such as the receiver device 104 or 202,while other aspects of the method may be performed at a controllerdevice, such as the controller device 108 or 302. In general, the method600 may be performed by a system comprising circuitry configured toimplement the various steps of the method 600. The circuitry may becomprised in a single electronic device, or may be distributed withinmore than one electronic device. The circuitry may comprise variouscombinations of processors (including DSPs), computer-readable mediastoring computer-executable instructions or code, ASICs, and the like.

At 602, an indirect FEC decoding property is measured at an electronicdevice. The measurement of the indirect FEC decoding property resultsfrom (or is associated with) applying FEC decoding to FEC-encoded bitsor symbols at a receiver device deployed in a communication network. Forexample, the indirect FEC decoding property may be measured as a resultof the receiver device 202 applying the FEC decoding 214 to FEC-encodedbits or symbols. The indirect FEC decoding property may be stored aspart of the measured FEC data 216 in the ASIC 210 of the receiver device202, or in the memory 308 of the controller device 302, or both.According to some examples, the indirect FEC decoding property maycomprise a statistical distribution. According to one example, theindirect FEC decoding property may comprise a distribution of a numberof iterations of a FEC decoding operation applied to each of a pluralityof FEC blocks processed within a period of time. In this case, theindirect FEC decoding property may comprise one or more histograms, oneor more PMFs, or both. According to some examples, the indirect FECdecoding property may further comprise a measured noise power of theplurality of FEC blocks processed during the period of time. Accordingto some examples, the measured noise power may be represented as a NSRvalue or a SNR value.

At 604, reference FEC data is optionally stored at an electronic device.For example, the reference FEC data 218 may be stored in the ASIC 210 ofthe receiver device 202, or in the memory 308 of the controller device302, or both. In general, the reference FEC data stored at 604 maycomprise, or may be based on, a reference FEC decoding property that isassociated with the FEC decoding property measured at 602, but where thereference FEC decoding property is associated with application of theFEC decoding under predefined noise conditions. For example, where themeasured FEC decoding property measured at 602 comprises the number ofiterations of a FEC decoding operation applied to each of a plurality ofFEC blocks processed within a period of time, the reference FEC datastored at 604 may comprise a reference frequency distribution ofiterations of the FEC decoding operation applied to each of a pluralityof reference FEC blocks exhibiting a predefined noise distribution and apredefined noise power. For example, the predefined noise distributionmay be an uncorrelated noise distribution such as an AWGN distribution,and the predefined noise power may be substantially equal to a noisepower comprised in FEC iteration data measured at 602. According to someexamples, the reference FEC data may comprise one or more histograms,one or more PMFs, or both. According to some examples, the reference FECdata may comprise one or more masks. The masks may be defined based on areference FEC decoding property, as described with respect to FIG. 5.

At 606, an assessment of receiver operating conditions is provided basedon the indirect FEC decoding property measured at 602, and, optionally,based on the reference FEC data stored at 604. According to someexamples, the assessment may be generated by comparing the indirect FECdecoding property and the reference FEC data. According to someexamples, where the reference FEC data comprises one or more masks, theassessment may be generated by applying the one or more masks to theindirect FEC decoding property. According to one example, the assessmentis performed as a result of the assessment processing 220 implemented bythe ASIC 210. According to another example, the assessment is performedas a result of the processor 304 of the controller device 302 executingthe assessment code 320. The assessment may be provided by storingassessment results, for example, in the ASIC 210 or in a separate memoryof the receiver device 202, or in the memory 308 of the controllerdevice 302. The assessment may additionally be provided by displayingthe assessment results on a display screen of an electronic device.According to some examples, the assessment may be provided bytransmitting the assessment results from one electronic device toanother electronic device.

At 608, a change in one or more parameters of the communication networkmay optionally be initiated based on the assessment provided at 606. Thenetwork parameters may include, for example, any combination of channelpower, choice of modulation format, carrier recovery parameters or othermodem parameters, routing of a channel or its neighbours, and the like.According to some examples, changes may be initiated by controllerdevice 302 transmitting signals to one or more other devices in thecommunication network. For example, as a result of an assessmentprovided at 606, the processor 304 of the controller device 302 mayexecute the controller code 312, which may result in the transmission ofinstructions to other network devices, where the instructions result inthe adjustment of one or more network parameters. For example, where thecurrent BER measured at the receiver device indicates that the predictedmargin is large, and where an assessment provided at 606 indicates thatthe receiver device is operating in noise that is substantiallyuncorrelated, the controller device 302 may initiate an increase in datarate, based on a high level of confidence that the predicted margin isaccurate. In another example, where the current BER measured at thereceiver device indicates that the predicted margin is sufficient, andwhere an assessment provided at 606 indicates that the receiver deviceis operating in some degree of correlated noise, the controller device302 may initiate a decrease in data rate, since the system may be closerto failure than expected from the BER. The change(s) in networkparameters optionally initiated at 608 may be automatic in response tothe assessment provided at 606, or may be based on actions performed bya user. For example, in response to viewing an assessment of receiveroperating conditions on a display screen of the controller device 302,an administrator may initiate an adjustment of one or more networkparameters using a keyboard of the controller device 302.

The preceding examples have generally been described in the context ofturbo block codes, where the indirect FEC decoding property being usedfor assessing operating conditions of the receiver device is the numberof FEC iterations required to correct each block of FEC-encoded bits orsymbols. However, a similar approach may be used for other FEC designs,including FEC designs that do not rely on iterative decoding.

In one example, a FEC scheme may comprise, or make use of, one or moreReed-Solomon (RS) decoding operations. Each RS decoding operation mayoperate on blocks of n q-bit symbols and, through the design of the FECscheme, may correct up to t symbol errors. In this case, the indirectFEC decoding property used in the assessment of receiver operatingconditions could comprise a statistical distribution of the number ofsymbol errors that were corrected each time the RS decoding operationwas used.

In another example, a FEC scheme may comprise, or make use of, one ormore staircase FEC decoding operations. In a staircase code, hard orsoft bits are placed into blocks and arranged in a staircaseconfiguration. Bose-Chaudhuri-Hocquenghem (BCH) parity bits arecalculated across pairs of adjoining blocks, either from rows of bits(for blocks which are beside each other), or from columns of bits (forblocks arranged vertically). A continuous stream of blocks may passthrough the FEC decoding such that, at any given time, the FEC decodingis operating on a particular group of blocks. The FEC decoding worksfrom the oldest, least noisy blocks to the newest, noisiest blocks, withthe BCH code being applied across rows in blocks that are beside eachother, and then along columns for blocks arranged vertically. Thesequence repeats until either (1) the syndromes in the oldest blocks ofthe group are all zero; or (2) a maximum number of iterations isreached. At this point, the oldest block exits the group, and the newestblock enters. As with simple turbo block codes, the indirect FECdecoding property used in the assessment of receiver operatingconditions could comprise a statistical distribution of the number ofiterations required before a given block exits the group. Alternatively,the indirect FEC decoding property could consider the effort requiredfor each iteration. For example, as errors between adjoining pairs ofblocks are corrected, it is no longer necessary to run the BCH decoderfor some rows or columns on subsequent iterations. The indirect FECdecoding property could comprise a statistical distribution of thenumber of BCH calls required to process each FEC block or the number ofBCH calls in some amount of time. The variation in the rate of BCH callscould be counted directly with circuitry in the ASIC or inferred throughmetrics such as the current consumption of the ASIC or its temperature.

In cases where a FEC design comprises a combination of two or more FECschemes, it is contemplated that the indirect FEC decoding property usedin the assessment of receiver operation conditions could comprise acombination of statistical results associated with applying the two ormore FEC schemes. For example, compression codes, such as thosedescribed by Oveis Gharan et al. in U.S. Pat. Application PublicationNo. 20190081730, may make use of iterative BCH decoding as well asintermediate RS error correction of the reconstructed syndrome bits. Inthis topology, the indirect FEC decoding property could include aprobability density function (PDF) of the number of BCH iterations, aswell as a PDF of the number of errors corrected by some combination ofthe RS decoders.

According to some examples, FEC statistics may be used to estimate howclose the FEC is to failure. FEC overhead may be adapted to make the FECmore resilient to noise at the cost of rate. FEC parameters could alsobe chosen to trade off power usage versus noise tolerance if theproximity to failure is well understood.

It may be advantageous to determine information about how noise isdistributed over the FEC blocks undergoing FEC decoding at a receiverdevice in a communication network. As will be described with respect toFIGS. 7-10, a measured FEC iteration distribution may be used tocalculate an estimate of a noise distribution over a plurality of FECblocks that are entering the FEC decoding.

A large population of bits may exhibit some average noise power. If thatpopulation is divided into smaller subsets, the average noise power ofthe bits within any given subset may differ somewhat from the averagenoise power of the entire population. In other words, there may be arange or distribution of noise power values across the subsets. Assuminguncorrelated white noise, a decrease in the number of bits within agiven subset may result in a wider distribution of the noise powervalues. An increase in the number of bits within a given subsetincreases the averaging and may result in a narrower distribution ofnoise power values. The variation in noise power across finite subsetsof a larger population may be referred to as numbers noise.

As previously described, a receiver device may output an average BERover a plurality of FEC blocks processed by FEC decoding during aspecific period of time. From the average BER, an indication of theaverage NSR over the plurality of FEC blocks may be determined. If onewere to measure the NSR value for each individual FEC block, it isexpected that numbers noise would result in some variation in thesevalues about the average NSR. For each value of NSR per FEC block,denoted NSR_(BLK), there may exist a respective probability ofoccurrence of that NSR, denoted P(NSR_(BLK)). The variation in NSRvalues over a plurality of FEC blocks may be illustrated using a PDF.

FIG. 7 illustrates two example noise PDFs over a plurality of FEC blocksin accordance with some examples of the technology disclosed herein.

The dashed curve illustrates an example noise PDF, which will herein bereferred to as a trial noise PDF for reasons that will later becomeapparent. Each point on the dashed curve represents the probability of aFEC block having a particular value NSR_(BLK) of noise power, whereNSR_(BLK) is shown in linear units. According to one example, the trialnoise PDF may be calculated using computer simulations performed on aplurality of simulated FEC blocks having a particular average NSR andassuming a particular noise distribution such as AWGN. Alternatively,the trial noise PDF could be calculated using analytic formulae orempirical models. The ranges of PDF values and NSR_(BLK) values providedin FIG. 7 are for illustrative purposes only and should not beconsidered necessarily limiting. NSR_(BLK) is shown in linear units.

The actual distribution of NSR_(BLK) values over a plurality of FECblocks may not be directly measured. However, as will now be described,an estimate of the NSR_(BLK) distribution may be calculated based on acombination of measured FEC iteration data and reference FEC iterationdata.

The relationship between a measured FEC iteration PMF over a pluralityof FEC blocks and the noise distribution of those FEC blocks may beexpressed by Equation 1:

P(N _(ITER))=Σ_(NSR) _(BLK) P(N _(ITER)|NSR_(BLK))·P(NSR_(BLK))  [1]

where P(N_(ITER)) denotes the probability of a number of FEC iterationsN_(ITER) being applied to any given FEC block of the plurality of FECblocks; where P(NSR_(BLK)) denotes the probability of a NSR valueNSR_(BLK) being observed in any given FEC block of the plurality of FECblocks; and where P(N_(ITER)|NSR_(BLK)) denotes the probability of anumber of FEC iterations N_(ITER) being applied to any given FEC blockof a plurality of FEC blocks, under the condition that each FEC block inthe plurality has the same noise power NSR_(BLK). According to oneexample, values for the conditional probability P(N_(ITER)|NSR_(BLK))may be calculated using computer simulations over a plurality ofsimulated FEC blocks filled with a well defined noise, such asuncorrelated AWGN. For example, for a given value of NSR_(BLK), acorresponding FEC iteration distribution P(N_(ITER)|NSR_(BLK)) may becomputed by applying FEC decoding to a plurality of FEC blocks, whereeach FEC block has the same given value of NSR_(BLK), and where thedistribution of noise is permitted to vary within each block accordingto an AWGN distribution. In this manner, a plurality of reference FECiteration distributions P(N_(ITER)|NSR_(BLK)) may be generated, one foreach value of NSR_(BLK). According to other examples, each reference FECiteration distribution P(N_(ITER)|NSR_(BLK)) may be generated throughanalytic calculations or through calibration under known noiseconditions. The plurality of reference FEC iteration distributions maybe represented by a series of PMFs.

FIG. 8 illustrates a series of reference FEC iteration PMFs. Eachdifferent PMF may be calculated, for example, from a different pluralityof simulated FEC blocks exhibiting a different value of NSR_(BLK). Acomparison of the reference FEC iteration PMFs in FIG. 8 shows that anincrease in the value of NSR_(BLK) generally results in an increasedprobability that a higher number of FEC iterations N_(ITER) will beapplied during FEC decoding. In other words, under the assumption ofAWGN, FEC blocks which exhibit a higher noise power are more likely torequire a higher number of FEC iterations during FEC decoding. TheNSR_(BLK) values and the ranges of PMF values and N_(ITER) values arefor illustrative purposes only and should not be considered necessarilylimiting.

According to Equation 1, the FEC iteration probability distributionP(N_(ITER)) may be calculated as a sum of the conditional FEC iterationprobability distribution P(N_(ITER)|NSR_(BLK)) weighted by the noiseprobability distribution P(NSR_(BLK)) over all values of NSR_(BLK). Bycombining a particular noise per block PDF, such as one of those shownin FIG. 7, with a series of reference FEC iteration PMFs, such as thoseshown in FIG. 8, it may be possible to predict the FEC iterationdistribution that would result from that particular noise PDF.

FIG. 9 illustrates two example FEC iteration PMFs. The dashed curveillustrates a trial FEC iteration PMF which may be calculated accordingto Equation 1 using the trial noise PDF illustrated in FIG. 7 and theseries of reference FEC iteration PMFs illustrated in FIG. 8. The trialFEC iteration PMF is effectively a weighted sum of the series ofreference FEC iteration PMFs, where the weighting is defined by thetrial noise PDF. The ranges of PMF values and N_(ITER) values are forillustrative purposes only and should not be considered necessarilylimiting.

The solid curve in FIG. 9 illustrates an example measured FEC iterationPMF, denoted P(N_(ITER)), determined from data output at a receiverdevice deployed in a communication network. According to some examples,an iterative process may be used to determine the trial noise PDF thatproduces the trial FEC iteration PMF that best matches the measured FECiteration PMF. For example, it may be assumed that the noise probabilityP(NSR_(BLK)) follows one of a Gompertz, exponential, beta prime, χ²,gamma, or other distribution defined, for example, by one or moreunknown constants a₀, a₁, . . . , a_(N), where Nis an integer. Thistrial noise probability distribution, denoted P(NSR_(BLK); a₀, a₁, . . ., a_(N)), may be assigned preliminary values of a₀, a₁, . . . , a_(N),and may be input into Equation 1 to generate a trial FEC iteration PMF,denoted P′(N_(ITER); a₀, a₁, . . . , a_(N)). A nonlinear solver may beused to determine the values of a₀, a₁, . . . , a_(N) that minimize adifference between the trial distribution P′(N_(ITER); a₀, a₁, . . . ,a_(N)) and the measured distribution P(N_(ITER)), such as the meansquare error, according to Equation 2:

$\begin{matrix}{\begin{matrix}{\arg\;\min} \\{a_{0},a_{1},\ldots\mspace{14mu},a_{N}}\end{matrix}\left\{ {{{\log\left( {{P\left( N_{ITER} \right)} + \epsilon} \right)} - {\log\ \left( {{P^{\prime}\left( {{N_{ITER};a_{0}},\ a_{1},\ldots\mspace{14mu},\ a_{N}} \right)} + \epsilon} \right)}}}^{2} \right\}} & \lbrack 2\rbrack\end{matrix}$

where ϵ is chosen to be smaller than the smallest values of P(N_(ITER)),thereby ensuring that the logarithm is real valued. In one example,ϵ=10⁻¹².

The concept of solving for the trial noise PDF that minimizes thedifference between the trial FEC iteration PMF and the measured FECiteration PMF is shown schematically through arrows in FIGS. 7 and 9.For each trial noise PDF, a corresponding trial FEC iteration PDF iscalculated. The parameters a₀, a₁, . . . , a_(N) are adjusted and thecalculation is repeated iteratively. With each iteration, the trial FECiteration PMF may move closer to the measured FEC iteration PMF, asdenoted by the arrows in FIG. 9. Simultaneously, the trial noise PDF maymove closer to an accurate estimate of the true noise PDF, as denoted bythe arrows in FIG. 7. Once the trial FEC iteration PMF is within acertain margin of error of the measured FEC iteration PMF, the solvermay output the current values of a₀, a₁, . . . , a_(N). Assuming aparticular type of noise distribution, the values of a₀, a₁, . . . ,a_(N) output by the solver are used to calculate an estimated noisedistribution, such as the solid curve shown in FIG. 7. In this manner,it may be possible to use a measured FEC iteration PMF to estimate adistribution of the noise in the FEC blocks entering the FEC decoding.

FIG. 10 illustrates an example method 1000 for calculating an estimatednoise distribution over a plurality of FEC blocks based on a measuredFEC iteration distribution in accordance with some examples of thetechnology disclosed herein.

At 1002, a measured FEC iteration distribution is stored at anelectronic device. For example, the measured FEC iteration distributionmay be stored as part of the measured FEC data 216 in the ASIC 210 ofthe receiver device 202. Alternatively, the processor 304 of thecontroller device 302 may execute the code 310 which causes theprocessor 304 to store the measured FEC iteration distribution as partof the measured FEC data 216 in the memory 308 of the controller device302. The measured FEC iteration distribution may comprise a statisticaldistribution of a number of iterations of a FEC decoding operationapplied to each of a plurality of FEC blocks, within a period of time,at a receiver device deployed in a communication network. The measuredFEC iteration distribution may be generated as a result of the FECdecoding 214 being implemented by the ASIC 210. The measured FECiteration distribution may comprise one or more histograms, one or morePMFs, or both. In addition to storing the measured FEC iterationdistribution, a measured noise power of the plurality of FEC blocksprocessed during the period of time may also be stored as part of themeasured FEC data 216. The measured noise power may be represented, forexample, by a NSR value or a SNR value.

At 1004, a series of reference FEC iteration distributions is stored atan electronic device. Each reference FEC iteration distributioncomprises a distribution of a number of iterations of the FEC decodingoperation applied to FEC blocks under different predefined noiseconditions. For example, each different reference FEC iterationdistribution may comprise a computer-simulated distribution of a numberof iterations of the FEC decoding operation applied to simulated FECblocks of a different plurality of simulated FEC blocks, where eachsimulated FEC block within a given plurality is constructed with welldefined noise statistics. For example, FEC blocks within a givenplurality may contain uncorrelated AWGN where all blocks have the sameaverage noise power, and where each different plurality has a differentaverage noise power. The series of reference FEC iteration distributionsmay be stored as part of the reference FEC data 218 in the ASIC 210 or aseparate memory of the receiver device 202. In another example, theprocessor 304 of the controller device 302 may execute the code 310which causes the processor 304 to store the series of reference FECiteration distributions as part of the reference FEC data 218 in thememory 308 of the controller device 302. Each reference FEC iterationdistribution may comprise, for example, a histogram or a PMF. Theactions at 1004 are illustrated as being performed after the actions at1002. However, the actions at 1004 may alternatively be performed priorto the actions at 1002, or in parallel to the actions at 1002.

At 1006, an estimate is calculated of the noise distribution over theplurality of FEC blocks prior to application of the FEC decodingoperation. The estimated noise distribution may be calculated using themeasured FEC iteration distribution stored at 1002 and the series ofreference FEC iteration distributions stored at 1004. As described withrespect to FIGS. 7-9, the estimated noise distribution may be calculatedby determining the parameters of a trial noise distribution thatminimize the error between a trial FEC iteration distribution and themeasured FEC iteration distribution. For example, a trial FEC iterationdistribution may be calculated by combining a series of reference FECiteration PMFs stored at 1004 with a trial noise PDF, according toEquation 1. According to one example, the trial noise PDF follows aGompertz distribution with particular values of a₀ and a₁. The trialnoise distribution may be stored at an electronic device, for example,in the ASIC 210 or a separate memory of the receiver device 202, or inthe memory 308 of the controller device 302. According to one example,the trial FEC iteration distribution may be calculated as a result ofimplementing the assessment processing 220. According to anotherexample, the trial FEC iteration distribution may be calculated as aresult of the processor 304 executing the assessment code 320. Byapplying a nonlinear solver to optimize a function, such as Equation 2,to the measured FEC iteration distribution and the trial FEC iterationdistribution, it may be possible to solve for the optimum values of a₀,a₁, . . . , a_(N), thereby resulting in an estimate of the noisedistribution over the plurality of FEC blocks processed at the receiver.

The ability to determine an estimate of the noise distribution over aplurality of FEC blocks entering the FEC decoding based on a measuredFEC iteration distribution generated as a result of the FEC decoding mayprovide several advantages. For one, it is expensive to calculateNSR_(BLK) directly inside a modem, as it requires an estimate of thenoise on the individual bits within FEC blocks, and then calculation ofthe statistical properties of that noise. There are typically thousandsof bits in each FEC block and this operation would need to be repeatedover millions of FEC blocks to arrive at statistically significantresults. In contrast, the proposed method 1000 for estimating the noisedistribution is inexpensive because the number of iterations required toprocess a FEC block is known within a typical FEC implementation and thecircuitry to obtain a histogram of the FEC iteration data requires asmall number of operations which only need to be carried out once perFEC block. Furthermore, once an estimate of the noise distribution hasbeen obtained, it may be relatively straightforward to obtainpredictions of FEC performance under different noise loading conditions.

For example, given a measured FEC iteration PMF, the method 1000 may beused to estimate a noise PDF. It may be of interest to determine how anadditional predefined noise contribution might be expected to affect themeasured FEC iteration PMF. To make this prediction, the additionalpredefined noise contribution may be convolved with the estimated noisedistribution to determine a modified noise distribution, according toEquation 3:

P _(MOD)(NSR_(BLK))=P(NSR_(BLK))*P _(M)(NSR_(BLK))  [3]

where P(NSR_(BLK)) denotes the measured noise PDF, P_(M)(NSR_(BLK))denotes the additional predefined noise contribution, P_(MOD)(NSR_(BLK))denotes the modified noise distribution, and where the asterisk *denotes convolution.

According to some examples, the modified noise distributionP_(MOD)(NSR_(BLK)) may be used as a test noise distribution forobtaining a prediction of the FEC iteration distribution. In general,based on the relationship defined in Equation 1, it may be possible tocalculate a predicted FEC iteration distribution based on any test noisedistribution by combining the test noise distribution with a series ofreference FEC iteration distributions P(N_(ITER)|NSR_(BLK)). Thismodification of Equation 1 is shown in Equation 4:

P _(PRED)(N _(ITER))=Σ_(NSR) _(BLK) P(N _(ITER)|NSR_(BLK))·P_(TEST)(NSR_(BLK))  [4]

where P_(PRED)(N_(ITER)) denotes the predicted FEC iterationdistribution and P_(TEST)(NSR_(BLK)) denotes the test noisedistribution. In order to determine the predicted FEC iterationdistribution P_(PRED)(N_(ITER)) resulting from the modified noisedistribution P_(MOD)(NSR_(BLK)) calculated from Equation 3, Equation 4could be applied under the conditionP_(TEST)(NSR_(BLK))=P_(MOD)(NSR_(BLK)).

The examples described herein generally assume thatP_(TEST)(NSR_(BLK))=P_(MOD)(NSR_(BLK)), where P_(MOD)(NSR_(BLK)) iscalculated based on the estimated noise distribution obtained using themethod 1000 and the modification resulting from Equation 3. However,other sources of P_(TEST)(NSR_(BLK)) are contemplated. For example, thetest noise distribution P_(TEST)(NSR_(BLK)) may be estimated based onone or more of BER, mutual information, noise on groups of symbolsentering a soft decoder, and the like. In another example, U.S. Pat.Application No. 20190190648 to Reimer et al. describes a method forestimating the noise on groups of received symbols. This method may beused, along with a model for the receiver DSP, to estimate NSR_(BLK).

FIG. 11 illustrates an example estimated noise PDF over a plurality ofFEC blocks, an example additional predefined noise PDF, and an examplemodified noise PDF in accordance with some examples of the technologydisclosed herein. The ranges of PDF values and NSR_(BLK) values are forillustrative purposes only and should not be considered necessarilylimiting. NSR_(BLK) is shown in linear units.

The solid curve represents the estimated noise PDF. The dotted curverepresents the additional predefined noise PDF. The dashed curverepresents the modified noise PDF which results from the application ofEquation 3 to the solid curve and the dotted curve.

FIG. 12 illustrates an example measured FEC iteration PMF and an examplepredicted FEC iteration PMF resulting from the additional predefinednoise PDF applied in FIG. 11, in accordance with some examples of thetechnology disclosed herein. The ranges of PMF values and N_(ITER)values are for illustrative purposes only and should not be considerednecessarily limiting.

The solid curve represents the measured FEC iteration PMF from which theestimated noise PDF in FIG. 11 was calculated, according to the method1000. The dashed curve represents the predicted FEC iteration PMF thatis expected as a result of the modified noise PDF shown in FIG. 11. Thepredicted FEC iteration PMF is calculated by applying Equation 1 to themodified noise distribution. As is apparent from FIGS. 11 and 12, theintroduction of the additional predefined noise contribution results inan increased likelihood of higher noise power, and an increasedlikelihood of higher numbers of FEC iterations. The ability to predictchanges in the FEC iteration distribution based on changes in the noisedistribution may prove highly valuable. For example, a SLA may requirethat the FER does not exceed 10⁻¹². Given a current measured FECiteration distribution measured from a receiver device, and theresultant estimate of the noise distribution, an administrator maydetermine how much additional noise can be tolerated while stillmaintaining the required maximum FER. All this may be done throughcalculations, rather than actually testing the system with real noise.Moreover, the accuracy of such calculations may be high because thenoise distribution estimated using the method 1000 does not rely on anyassumptions about the origin of the noise entering the FEC decoding orits statistical properties.

FIG. 13 illustrates an example method 1300 for calculating a predictedFEC iteration distribution based on a test noise distribution, inaccordance with some examples of the technology disclosed herein.

At 1302, a modified noise distribution is optionally calculated over aplurality of FEC blocks based on an estimated noise distribution and anadditional predefined noise distribution. For example, in accordancewith Equation 3, an additional predefined noise contributionP_(M)(NSR_(BLK)) may be convolved with an estimated noise distributionP(NSR_(BLK)) to determine a modified noise distributionP_(MOD)(NSR_(BLK)). The estimated noise distribution P(NSR_(BLK)) mayhave been calculated according to the method 1000. According to oneexample, the modified noise distribution may be calculated as a resultof implementing the assessment processing 220. According to anotherexample, the modified noise distribution may be calculated as a resultof the processor 304 executing the assessment code 320.

At 1304, a test noise distribution over a plurality of FEC blocks isstored at an electronic device. For example, the test noise distributionmay be stored at an electronic device, for example, in the ASIC 210 or aseparate memory of the receiver device 202, or the memory 308 of thecontroller device 302, or some combination thereof. According to someexamples, the test noise distribution may comprise the modified noisedistribution P_(MOD)(NSR_(BLK)) calculated at 1302.

At 1306, a series of reference FEC iteration distributions is stored atan electronic device. As described with respect to the method 1000, eachreference FEC iteration distribution may comprise a distribution of anumber of iterations of a FEC decoding operation applied to FEC blocksunder different predefined noise conditions. According to some examples,the series of reference FEC iteration distributionsP(N_(ITER)|NSR_(BLK)) may have been generated using computersimulations, analytic calculations, or calibration using real hardware.The actions at 1306 are illustrated as being performed after the actionsat 1304. However, the actions at 1306 may alternatively be performedprior to the actions at 1304, or in parallel to the actions at 1304.

At 1308, a predicted FEC iteration distribution may be calculated basedon the test noise distribution stored at 1304 and the series ofreference FEC iteration distributions stored at 1306. For example, inaccordance with Equation 4, the test noise distributionP_(TEST)(NSR_(BLK)) stored at 1304 may be combined with the series ofreference FEC iteration distributions P(N_(ITER)|NSR_(BLK)) stored at1306 in order to obtain a predicted FEC iteration distributionP_(PRED)(N_(ITER)). According to one example, the predicted FECiteration distribution may be calculated as a result of implementing theassessment processing 220. According to another example, the predictedFEC iteration distribution may be calculated as a result of theprocessor 304 executing the assessment code 320.

At 1310, an assessment of predicted receiver operating conditions mayoptionally be provided based on the predicted FEC iteration distributioncalculated at 1308. For example, where the test noise distributionstored at 1304 is the modified noise distribution calculated at 1302,the assessment may comprise an indication of a predicted change inmargin as a result of the additional predefined noise. In anotherexample, the assessment may comprise an indication of whether or not thepredicted FEC iteration distribution satisfies certain requirements ofthe communication network. According to some examples, the assessmentmay involve one or more thresholds or masks, as described with respectto the method 600. According to one example, the assessment is performedas a result of implementing the assessment processing 220. According toanother example, the assessment is performed as a result of theprocessor 304 of the controller device 302 executing the assessment code320. The assessment may be provided by storing the assessment at anelectronic device, for example, in the ASIC 210 or a separate memory ofthe receiver device 202, or the memory 308 of the controller device 302.The assessment may additionally be provided by displaying results of theassessment on a display screen of an electronic device.

Although not explicitly shown in FIG. 13, the assessment provided at1310 may be used to make decisions regarding changes in thecommunication network. For example, one may contemplate a scenario inwhich a particular change in a network is expected to result in themodified noise distribution as calculated at 1302. If an assessmentresult provided at 1310 indicates that the predicted FEC iterationdistribution calculated at 1308 is in compliance with certain networkrequirements, an administrator may make a decision to apply theparticular change in the network. On the other hand, an assessmentresult provided at 1310 indicates that the predicted FEC iterationdistribution calculated at 1308 is no longer in compliance with certainnetwork requirements, the administrator may make a decision not to applythe particular change in the network. Changes in the network couldinclude changes to various network parameters, such as channel power,choice of modulation format, FED coding, carrier recovery parameters orother modem parameters, routing of a channel or its neighbours, and thelike.

The preceding examples described with respect to FIGS. 7-13 andEquations 1-4 generally involve an anticipated relationship between thenoise distribution of the FEC blocks and the observed FEC iterationdistribution. For example, the method 1000 uses a measured FEC iterationdistribution to calculate an estimate of the noise distribution of theFEC blocks based on the anticipated relationship defined in Equation 1.It is contemplated that anticipated relationships may be establishedbetween the noise distribution of the FEC blocks and FEC statisticsother than the FEC iteration distribution. For example, an equationsimilar to Equation 1 may define an anticipated relationship between thenoise distribution of the FEC blocks and indirect FEC decoding propertysuch as heat, temperature, current, voltage, active clock cycles, idleclock cycles, activity of parallel engines, or activity of pipelinestages. In this manner, it may be possible to start from a measureddistribution of a particular FEC decoding property, and solve for thestatistical properties of the noise entering the FEC decoding.

The scope of the claims should not be limited by the details set forthin the examples, but should be given the broadest interpretationconsistent with the description as a whole.

1. A system comprising: circuitry configured to measure an indirectforward error correction (FEC) decoding property associated withapplying FEC decoding to FEC-encoded bits or symbols at a receiverdevice deployed in a communication network, the FEC-encoded bits orsymbols having been encoded using FEC encoding corresponding to the FECdecoding; and circuitry configured to provide an assessment of operatingconditions of the receiver device based on the indirect FEC decodingproperty.
 2. The system as claimed in claim 1, wherein the indirect FECdecoding property comprises a number of iterations of a FEC decodingoperation applied to one or more FEC blocks at the receiver device whilethe receiver device is deployed in the communication network, each ofthe FEC blocks comprising a plurality of the FEC-encoded bits orsymbols.
 3. The system as claimed in claim 2, wherein the indirect FECdecoding property comprises a distribution of the number of iterationsof the FEC decoding operation applied to each of a plurality of the FECblocks processed within a period of time.
 4. (canceled)
 4. The system asclaimed in claim 1, further comprising circuitry configured to storereference FEC data, wherein the assessment is based on a comparisonbetween the indirect FEC decoding property and the reference FEC data.5. The system as claimed in claim 4, wherein the reference FEC datacomprises one or more masks.
 6. The system as claimed in claim 4,wherein the reference FEC data is based on a reference FEC decodingproperty associated with application of the FEC decoding underpredefined noise conditions.
 7. The system as claimed in claim 1,wherein the assessment of the operating conditions comprisesclassification of the receiver device into at least one of one or morepredefined categories.
 9. The system as claimed in claim 1, wherein thereceiver device comprises the circuitry configured to measure theindirect FEC decoding property, the system further comprising acontroller device, wherein the controller device comprises circuitryconfigured to initiate a change in one or more parameters of thecommunication network based on the assessment of the operatingconditions of the receiver device.
 10. The system as claimed in claim 9,wherein the parameters of the communication network comprise one or moreof data rate, launch power, transmission distance, channel spacing,add-drop filter configuration, and network routing.
 11. The system asclaimed in claim 9, wherein the controller device comprises thecircuitry configured to provide the assessment of the operatingconditions of the receiver device.
 12. The system as claimed in claim 1,wherein providing the assessment comprises one or more of storingassessment results at one or more electronic devices in thecommunication network; transmitting assessment results from oneelectronic device in the communication network to another electronicdevice in the communication network; and displaying assessment resultson a display screen of at least one electronic device in thecommunication network.
 13. An electronic device comprising: circuitryconfigured to store a measured forward error correction (FEC) iterationdistribution of a number of iterations of a FEC decoding operationapplied to each of a plurality of FEC blocks processed, within a periodof time, at a receiver device deployed in a communication network, eachFEC block consisting of FEC-encoded bits or symbols encoded using a FECencoding operation corresponding to the FEC decoding operation; andcircuitry configured to store a series of reference FEC iterationdistributions, each reference FEC iteration distribution comprising adistribution of a number of iterations of the FEC decoding operationapplied to FEC blocks under different predefined noise conditions; andcircuitry configured to calculate, based on the measured FEC iterationdistribution and the series of reference FEC iteration distributions, anestimate of a noise distribution over the plurality of FEC blocks priorto application of the FEC decoding operation.
 14. The electronic deviceas claimed in claim 13, further comprising circuitry configured tocalculate a trial FEC iteration distribution by combining a trial noisedistribution having trial parameters with the series of reference FECiteration distributions; and circuitry configured to calculate theestimate of the noise distribution by determining the trial parameterswhich minimize a difference between the trial FEC iteration distributionand the measured FEC iteration distribution.
 15. The electronic deviceas claimed in claim 13, further comprising circuitry configured tocalculate a modified noise distribution based on the estimate of thenoise distribution and an additional predefined noise distribution; andcircuitry configured to calculate a predicted FEC iteration distributionbased on the modified noise distribution and the series of reference FECiteration distributions.
 16. The electronic device as claimed in claim15, further comprising circuitry configured to provide an assessment ofa predicted change in operating conditions of the receiver device basedon the predicted FEC iteration distribution; and circuitry configured toinitiate a change in one or more parameters of the communication networkbased on the assessment.
 17. (canceled)
 18. An electronic devicecomprising: circuitry configured to store a test noise distribution overa plurality of FEC blocks, each FEC block consisting of FEC-encoded bitsor symbols encoded using a FEC encoding operation; circuitry configuredto store a series of reference FEC iteration distributions, eachreference FEC iteration distribution comprising a distribution of anumber of iterations of a FEC decoding operation applied to FEC blocksunder different predefined noise conditions, the FEC decoding operationcorresponding to the FEC encoding operation; and circuitry configured tocalculate a predicted FEC iteration distribution based on the test noisedistribution and the series of reference FEC iteration distributions.19. The electronic device as claimed in claim 18, further comprisingcircuitry configured to provide an assessment of predicted operatingconditions of a receiver device based on the predicted FEC iterationdistribution.
 20. The electronic device as claimed in claim 19, furthercomprising circuitry configured to initiate a change in one or moreparameters of a communication network based on the assessment.
 21. Thesystem as claimed in claim 1, wherein the indirect FEC decoding propertycomprises any one of heat, temperature, current, voltage, active clockcycles, idle clock cycles, activity of parallel engines, activity ofpipeline stages, input buffer fill level of the FEC decoding, and outputbuffer fill level of the FEC decoding.